# import pandas as pd
# import numpy as np
# import os
import sys
# import model_code.gen_data as gd
# import model_code.rfg as rfg
# import model_code.per_mea as pm
# from tensorflow import keras

# source = sys.argv[1] #tongji
# pre_time = sys.argv[2]#5
# ioh_time = sys.argv[3] #1
# ob_win = sys.argv[4] #5
source = "tongji"
pre_time = sys.argv[1]
ioh_time = "1"
ob_wins = [5,10,15]
pre_time = int(pre_time)
pre_time = pre_time / 5
for ob_win in ob_wins:
    if pre_time == 1.0 and ob_win == 5:
        continue
    ob_win = int(ob_win)
    d_path = source + "/dynamic_normalization/" + ioh_time + "-bt.csv"
        #tongji/dynamic_normalization/1-bt.csv"
    c_path = "config_bt.json"
    print(source,pre_time,ob_win)

# static, dynamic, label = gd.gen_data(source, d_path, c_path, pre_time, ob_win)

# r = label.sum()
# l = label.shape[0] - r
# r = l / r
# if r < 1:
#     r = 1
# l = 1
# cw = {0: l, 1: r}
# print("cw: 1, " + str(r))

# model_path = "models/" + source + "-" + str(pre_time) + "-" + ioh_time + "-" + str(ob_win) + ".h5"
# #model_path = "models/tongji-1.0-1-5.h5"模型存储路径

# model = rfg.create_model_2(dynamic.shape[1:], ob_win)

# model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy', pm.AUC])

# dynamic_dim = dynamic.reshape(dynamic.shape[0], dynamic.shape[1], dynamic.shape[2], 1)

# history = model.fit([dynamic_dim, dynamic], label, epochs=200, batch_size=1024,class_weight=cw,
#                     validation_split=0.3, verbose=2,
#                    callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='min'),
#                               keras.callbacks.ModelCheckpoint(model_path, monitor='val_loss', save_best_only=True, mode='min', verbose=0)])
